The broad objective of this proposal is to improve the health of elderly postmenopausal women through prevention of osteoporosis and fractures. Osteoporosis disproportionately affects elderly women and is associated with fractures that result in pain, disability, death, and a large expenditure of health care resources. Given the increasing proportion of the population that is elderly, it is imperative that sensible prevention strategies be identified and implemented. Our proposed research will facilitate the identification of appropriate prevention strategies. By comparing the net cost of prevention with the additional health benefits provided, we will identify prevention strategies that improve health outcomes at a reasonable cost. This is especially important with the advent of costly therapies aimed at preventing bone loss in the elderly. Studies of hormone replacement therapy (HRT) among perimenopausal women indicate that patient preferences are important in the cost-effectiveness equation. However, no data from actual patients are currently available for assessing quality-adjusted life expectancy. Our proposal is to collect patient preference data for the purpose of estimating quality- adjusted life years and to integrate these data into a decision-analytic prevention model to identify cost-effective approaches for preventing osteoporosis in the elderly. There are three components to the proposed project. First, a decision- analytic prevention model will be developed that includes hip and vertebral fracture endpoints and integrates economic and epidemiologic data for HRT and non-HRT interventions. Second, data on patient preferences concerning fracture outcomes and treatment side-effects will be collected. Third, these data will be integrated into the prevention model to assess the impact of both selective and universal interventions to prevent osteoporosis in the elderly on life expectancy, quality- adjusted life expectancy, cost and cost-effectiveness. Extensive analyses with the integrated prevention model will allow us to identify areas of uncertainty that impact on the cost-effectiveness of competing approaches to prevention. Therefore, our results will be useful in prioritizing areas for future research.